"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from copy import deepcopy
from functools import partial

import torch
import torch.nn as nn

from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .layers import StdConv2dSame, StdConv2d, to_2tuple
from .resnet import resnet26d, resnet50d
from .resnetv2 import ResNetV2, create_resnetv2_stem
from .registry import register_model
from timm.models.vision_transformer import _create_vision_transformer


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
        'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
        'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
        'first_conv': 'patch_embed.backbone.stem.conv', 'classifier': 'head',
        **kwargs
    }


default_cfgs = {
    # hybrid in-1k models (weights from official JAX impl where they exist)
    'vit_tiny_r_s16_p8_224': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz',
        first_conv='patch_embed.backbone.conv'),
    'vit_tiny_r_s16_p8_384': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
        first_conv='patch_embed.backbone.conv', input_size=(3, 384, 384), crop_pct=1.0),
    'vit_small_r26_s32_224': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'R26_S_32-i21k-300ep-lr_0.001-aug_light0-wd_0.03-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.03-res_224.npz',
    ),
    'vit_small_r26_s32_384': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz',
        input_size=(3, 384, 384), crop_pct=1.0),
    'vit_base_r26_s32_224': _cfg(),
    'vit_base_r50_s16_224': _cfg(),
    'vit_base_r50_s16_384': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
        input_size=(3, 384, 384), crop_pct=1.0),
    'vit_large_r50_s32_224': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'R50_L_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz'
    ),
    'vit_large_r50_s32_384': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/'
            'R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz',
        input_size=(3, 384, 384), crop_pct=1.0
    ),

    # hybrid in-21k models (weights from official Google JAX impl where they exist)
    'vit_tiny_r_s16_p8_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz',
        num_classes=21843, crop_pct=0.9, first_conv='patch_embed.backbone.conv'),
    'vit_small_r26_s32_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0.npz',
        num_classes=21843, crop_pct=0.9),
    'vit_base_r50_s16_224_in21k': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth',
        num_classes=21843, crop_pct=0.9),
    'vit_large_r50_s32_224_in21k': _cfg(
        url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0.npz',
        num_classes=21843, crop_pct=0.9),

    # hybrid models (using timm resnet backbones)
    'vit_small_resnet26d_224': _cfg(
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
    'vit_small_resnet50d_s16_224': _cfg(
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
    'vit_base_resnet26d_224': _cfg(
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
    'vit_base_resnet50d_224': _cfg(
        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'),
}


class HybridEmbed(nn.Module):
    """ CNN Feature Map Embedding
    Extract feature map from CNN, flatten, project to embedding dim.
    """
    def __init__(self, backbone, img_size=224, patch_size=1, feature_size=None, in_chans=3, embed_dim=768):
        super().__init__()
        assert isinstance(backbone, nn.Module)
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.backbone = backbone
        if feature_size is None:
            with torch.no_grad():
                # NOTE Most reliable way of determining output dims is to run forward pass
                training = backbone.training
                if training:
                    backbone.eval()
                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
                if isinstance(o, (list, tuple)):
                    o = o[-1]  # last feature if backbone outputs list/tuple of features
                feature_size = o.shape[-2:]
                feature_dim = o.shape[1]
                backbone.train(training)
        else:
            feature_size = to_2tuple(feature_size)
            if hasattr(self.backbone, 'feature_info'):
                feature_dim = self.backbone.feature_info.channels()[-1]
            else:
                feature_dim = self.backbone.num_features
        assert feature_size[0] % patch_size[0] == 0 and feature_size[1] % patch_size[1] == 0
        self.grid_size = (feature_size[0] // patch_size[0], feature_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        x = self.backbone(x)
        if isinstance(x, (list, tuple)):
            x = x[-1]  # last feature if backbone outputs list/tuple of features
        x = self.proj(x).flatten(2).transpose(1, 2)
        return x


def _create_vision_transformer_hybrid(variant, backbone, pretrained=False, **kwargs):
    embed_layer = partial(HybridEmbed, backbone=backbone)
    kwargs.setdefault('patch_size', 1)  # default patch size for hybrid models if not set
    return _create_vision_transformer(
        variant, pretrained=pretrained, embed_layer=embed_layer, default_cfg=default_cfgs[variant], **kwargs)


def _resnetv2(layers=(3, 4, 9), **kwargs):
    """ ResNet-V2 backbone helper"""
    padding_same = kwargs.get('padding_same', True)
    stem_type = 'same' if padding_same else ''
    conv_layer = partial(StdConv2dSame, eps=1e-8) if padding_same else partial(StdConv2d, eps=1e-8)
    if len(layers):
        backbone = ResNetV2(
            layers=layers, num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
            preact=False, stem_type=stem_type, conv_layer=conv_layer)
    else:
        backbone = create_resnetv2_stem(
            kwargs.get('in_chans', 3), stem_type=stem_type, preact=False, conv_layer=conv_layer)
    return backbone


@register_model
def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs):
    """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
    """
    backbone = _resnetv2(layers=(), **kwargs)
    model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_tiny_r_s16_p8_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_tiny_r_s16_p8_384(pretrained=False, **kwargs):
    """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 384 x 384.
    """
    backbone = _resnetv2(layers=(), **kwargs)
    model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_tiny_r_s16_p8_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_r26_s32_224(pretrained=False, **kwargs):
    """ R26+ViT-S/S32 hybrid.
    """
    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_small_r26_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_r26_s32_384(pretrained=False, **kwargs):
    """ R26+ViT-S/S32 hybrid.
    """
    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_small_r26_s32_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_r26_s32_224(pretrained=False, **kwargs):
    """ R26+ViT-B/S32 hybrid.
    """
    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_base_r26_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_r50_s16_224(pretrained=False, **kwargs):
    """ R50+ViT-B/S16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
    """
    backbone = _resnetv2((3, 4, 9), **kwargs)
    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_base_r50_s16_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_r50_s16_384(pretrained=False, **kwargs):
    """ R50+ViT-B/16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer.
    """
    backbone = _resnetv2((3, 4, 9), **kwargs)
    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_base_r50_s16_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_resnet50_384(pretrained=False, **kwargs):
    # DEPRECATED this is forwarding to model def above for backwards compatibility
    return vit_base_r50_s16_384(pretrained=pretrained, **kwargs)


@register_model
def vit_large_r50_s32_224(pretrained=False, **kwargs):
    """ R50+ViT-L/S32 hybrid.
    """
    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
    model_kwargs = dict(embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_large_r50_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_large_r50_s32_384(pretrained=False, **kwargs):
    """ R50+ViT-L/S32 hybrid.
    """
    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
    model_kwargs = dict(embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_large_r50_s32_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_tiny_r_s16_p8_224_in21k(pretrained=False, **kwargs):
    """ R+ViT-Ti/S16 w/ 8x8 patch hybrid.  ImageNet-21k.
    """
    backbone = _resnetv2(layers=(), **kwargs)
    model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_tiny_r_s16_p8_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_r26_s32_224_in21k(pretrained=False, **kwargs):
    """ R26+ViT-S/S32 hybrid. ImageNet-21k.
    """
    backbone = _resnetv2((2, 2, 2, 2), **kwargs)
    model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_small_r26_s32_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_r50_s16_224_in21k(pretrained=False, **kwargs):
    """ R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    """
    backbone = _resnetv2(layers=(3, 4, 9), **kwargs)
    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_base_r50_s16_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
    # DEPRECATED this is forwarding to model def above for backwards compatibility
    return vit_base_r50_s16_224_in21k(pretrained=pretrained, **kwargs)


@register_model
def vit_large_r50_s32_224_in21k(pretrained=False, **kwargs):
    """ R50+ViT-L/S32 hybrid. ImageNet-21k.
    """
    backbone = _resnetv2((3, 4, 6, 3), **kwargs)
    model_kwargs = dict(embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_large_r50_s32_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_resnet26d_224(pretrained=False, **kwargs):
    """ Custom ViT small hybrid w/ ResNet26D stride 32. No pretrained weights.
    """
    backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
    model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_small_resnet26d_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_small_resnet50d_s16_224(pretrained=False, **kwargs):
    """ Custom ViT small hybrid w/ ResNet50D 3-stages, stride 16. No pretrained weights.
    """
    backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[3])
    model_kwargs = dict(embed_dim=768, depth=8, num_heads=8, mlp_ratio=3, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_small_resnet50d_s16_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_resnet26d_224(pretrained=False, **kwargs):
    """ Custom ViT base hybrid w/ ResNet26D stride 32. No pretrained weights.
    """
    backbone = resnet26d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_base_resnet26d_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model


@register_model
def vit_base_resnet50d_224(pretrained=False, **kwargs):
    """ Custom ViT base hybrid w/ ResNet50D stride 32. No pretrained weights.
    """
    backbone = resnet50d(pretrained=pretrained, in_chans=kwargs.get('in_chans', 3), features_only=True, out_indices=[4])
    model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer_hybrid(
        'vit_base_resnet50d_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
    return model